There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Arian Ashourvan |
| Institution: | University of Kansas |
| Department: | Psychology |
| Country: | |
| Proposed Analysis: | I propose to investigate the structural connectivity networks in patients with Alzheimer's disease (AD) using algebraic topological tools. By analyzing the topology of these networks, I aim to gain insights into how changes in their topological features can serve as potential predictors of patient subtypes and as more sensitive biomarkers of AD progression. Algebraic topology offers a powerful framework for studying the properties and transformations of complex networks. By applying algebraic topological methods, such as persistent homology or graph spectra analysis, to the structural connectivity networks of AD patients, I can capture and quantify key topological characteristics. Through this research, I seek to identify specific network features that distinguish different subtypes of AD patients. By leveraging the inherent structural alterations in the connectivity networks, I aim to develop more refined and sensitive biomarkers for tracking disease progression. Understanding how changes in the topological properties of structural connectivity networks relate to AD subtypes can have significant implications for personalized diagnosis, prognosis, and treatment. It may provide insights into the underlying mechanisms of the disease and potentially guide the development of targeted interventions or therapies. By combining algebraic topological tools with the study of structural connectivity networks in AD, this research has the potential to contribute to a deeper understanding of the disease and pave the way for improved diagnostic and prognostic approaches. |
| Additional Investigators |
